



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	KandinskyVaaInpaintPipeline
			A      		=	['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
			A      		=	[
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			    '''mask_image''',
			]
			A      		=	[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			A      		=	False

			@property
			def      __snake_case       (self					)       ->   List[Any]:
						return 32

			@property
			def      __snake_case       (self					)       ->   Optional[int]:
						return 32

			@property
			def      __snake_case       (self					)       ->   Any:
						return self.time_input_dim

			@property
			def      __snake_case       (self					)       ->   int:
						return self.time_input_dim * 4

			@property
			def      __snake_case       (self					)       ->   Any:
						return 100

			@property
			def      __snake_case       (self					)       ->   Optional[Any]:
						torch.manual_seed(0					)

						UpperCAmelCase_:		Optional[int]							      =     {
						    """in_channels""": 9,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						UpperCAmelCase_:		str							      =     UNetaDConditionModel(**SCREAMING_SNAKE_CASE_					)
						return model

			@property
			def      __snake_case       (self					)       ->   Tuple:
						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}

			@property
			def      __snake_case       (self					)       ->   Tuple:
						torch.manual_seed(0					)
						UpperCAmelCase_:		Optional[Any]							      =     VQModel(**self.dummy_movq_kwargs					)
						return model

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		List[str]							      =     self.dummy_unet
						UpperCAmelCase_:		int							      =     self.dummy_movq

						UpperCAmelCase_:		List[Any]							      =     DDIMScheduler(
						    num_train_timesteps=1000,   beta_schedule="""linear""",   beta_start=0.0_0_0_8_5,   beta_end=0.0_1_2,   clip_sample=SCREAMING_SNAKE_CASE_,   set_alpha_to_one=SCREAMING_SNAKE_CASE_,   steps_offset=1,   prediction_type="""epsilon""",   thresholding=SCREAMING_SNAKE_CASE_,   )

						UpperCAmelCase_:		Any							      =     {
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=0					)       ->   Dict:
						UpperCAmelCase_:		List[str]							      =     floats_tensor((1, self.text_embedder_hidden_size),   rng=random.Random(SCREAMING_SNAKE_CASE_					)					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     floats_tensor((1, self.text_embedder_hidden_size),   rng=random.Random(seed + 1					)					).to(
						    SCREAMING_SNAKE_CASE_					)
						# create init_image
						UpperCAmelCase_:		Tuple							      =     floats_tensor((1, 3, 64, 64),   rng=random.Random(SCREAMING_SNAKE_CASE_					)					).to(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     image.cpu().permute(0,   2,   3,   1					)[0]
						UpperCAmelCase_:		Any							      =     Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_					)					).convert("""RGB"""					).resize((256, 256)					)
						# create mask
						UpperCAmelCase_:		Optional[Any]							      =     np.ones((64, 64),   dtype=np.floataa					)
						UpperCAmelCase_:		Optional[int]							      =     0

						if str(SCREAMING_SNAKE_CASE_					).startswith("""mps"""					):
									UpperCAmelCase_:		Optional[Any]							      =     torch.manual_seed(SCREAMING_SNAKE_CASE_					)
						else:
									UpperCAmelCase_:		List[str]							      =     torch.Generator(device=SCREAMING_SNAKE_CASE_					).manual_seed(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     {
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		List[Any]							      =     """cpu"""

						UpperCAmelCase_:		Optional[int]							      =     self.get_dummy_components()

						UpperCAmelCase_:		List[str]							      =     self.pipeline_class(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     pipe.to(SCREAMING_SNAKE_CASE_					)

						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_					)					)
						UpperCAmelCase_:		Optional[Any]							      =     output.images

						UpperCAmelCase_:		Dict							      =     pipe(
						    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_					),   return_dict=SCREAMING_SNAKE_CASE_,   )[0]

						UpperCAmelCase_:		str							      =     image[0, -3:, -3:, -1]
						UpperCAmelCase_:		int							      =     image_from_tuple[0, -3:, -3:, -1]

						print(f'image.shape {image.shape}'					)

						assert image.shape == (1, 64, 64, 3)

						UpperCAmelCase_:		List[str]							      =     np.array(
						    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]					)

						assert (
						    np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
						), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
						), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'

			def      __snake_case       (self					)       ->   Dict:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3					)







@slow
@require_torch_gpu
class 				_a				(   unittest.TestCase					):

			def      __snake_case       (self					)       ->   Tuple:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""					)

						UpperCAmelCase_:		Any							      =     load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
						UpperCAmelCase_:		str							      =     np.ones((768, 768),   dtype=np.floataa					)
						UpperCAmelCase_:		str							      =     0

						UpperCAmelCase_:		Optional[Any]							      =     """a hat"""

						UpperCAmelCase_:		str							      =     KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior""",   torch_dtype=torch.floataa					)
						pipe_prior.to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     KandinskyVaaInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder-inpaint""",   torch_dtype=torch.floataa					)
						UpperCAmelCase_:		int							      =     pipeline.to(SCREAMING_SNAKE_CASE_					)
						pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     torch.Generator(device="""cpu"""					).manual_seed(0					)
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     pipe_prior(
						    SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   num_inference_steps=5,   negative_prompt="""""",   ).to_tuple()

						UpperCAmelCase_:		Tuple							      =     pipeline(
						    image=SCREAMING_SNAKE_CASE_,   mask_image=SCREAMING_SNAKE_CASE_,   image_embeds=SCREAMING_SNAKE_CASE_,   negative_image_embeds=SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   num_inference_steps=100,   height=768,   width=768,   output_type="""np""",   )

						UpperCAmelCase_:		Any							      =     output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)











import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






